Adherence to anti-hypertensive medication: proposing and testing a conceptual model

Br J Health Psychol. 2012 Feb;17(1):202-19. doi: 10.1111/j.2044-8287.2011.02034.x. Epub 2011 Jun 15.


Objectives: A conceptual model of the psychological factors underpinning adherence to anti-hypertensive medication is proposed and tested. The model suggests that adherence is influenced by three sets of variables: demography, health status, and perceived effects of medication; cognitions and motivation; and intention to adhere.

Methods and design: Patients with known hypertension were recruited from three primary care practices in South-East England and were asked to complete a postal questionnaire. A total of 1,070 responses were received. The questionnaire asked about the three sets of predictor variables, and adherence. Eight weeks after the first questionnaire, a second was posted to all respondents, this time asking about adherence over the intervening period.

Results: The three sets of predictor variables were treated as blocks in a hierarchical model, so that each successive block added to the variance in adherence explained by the previous blocks. The data were analysed by hierarchical multiple regression. The predictors accounted for 19% of the variance in adherence at Time 1, and 34% at Time 2. The leading individual predictors at Time 1 were age, gender, conscientiousness, hypertensive identity, perceived behavioural control, and intention. At Time 2, they were the same, except that gender made way for adherence at Time 1.

Conclusions: The model offers a parsimonious account, and the findings suggest a number of approaches to designing interventions to modify behaviour.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Aged
  • Aged, 80 and over
  • Antihypertensive Agents / therapeutic use*
  • England
  • Female
  • Humans
  • Hypertension / drug therapy
  • Intention
  • Male
  • Middle Aged
  • Models, Theoretical
  • Patient Compliance*
  • Surveys and Questionnaires


  • Antihypertensive Agents